design matrix(设计矩阵) 是统计学上的概念,一般标记为 X,是由一组对象的解释变量(explanatory variables)构成的矩阵。 1. explanatory variables 刻画的是属性列(feature column),也即一个样本、一个对象都可视为,或者抽象为,符号化为,一个多维向量,向量的每一个 component 表示一定的特征,比如身高,体重等信息,...
2019-12-12 17:50 −Canvas类中drawBitmap(Bitmap bitmap, Matrix matrix, Paint paint)方法中有个参数类型是Matrix,从字面上理解是矩阵的意思,而实际上它也确实是个3x3的矩阵。Matrix在Android中的主要作用是图像变换,如平移、旋转、缩放、扭曲等... ...
If the elements of L(u) are independent, then there exists a u⋆ such that L(u⋆)=L⋆(u) and this u⋆ makes the L2 gain less than or equal to γ. On the other hand, if the elements of L⋆(u) are dependent, a method of solution is suggested in subsequent sections. ...
Apart from this, density matrix (B.10) is completely general, independent of all dynamical models which one may like to use for calculating the photoionization amplitudes Pe(L1+*ℓp; L0 1; L), present therein, in the Russell–Saunders coupling in the absence of SOI....
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Reviewed eigenvectors/eigenvalues Ax=λx, which make a square matrix act like a scalar λ. For an m×m matrix A, you get m eigenvalues λₖ from the roots (possibly repeated) of thecharacteristic polynomialdet(A-λΙ), and you almost always get m independent eigenvalues xₖ (except in...
aUsing the notions of rank, condition number, size and the SVD, we can analyse whether or not the matrix is singular, how many independent columns exist, what columns are dependent and to what degree. 使用等级、情况数字、大小和SVD的概念,我们可以分析是否矩阵是单一的,多少个独立专栏存在,什么专...
et al. Externally applied local mechanical force induces growth of focal contacts by an mdia1-dependent and rock- independent mechanism. J. Cell Biol. 153, 1175–1186 (2001). 49. Cox, E. A. & Huttenlocher, A. Regulation of integrin-mediated adhesion during cell migration. Microsc. Res. ...
The Euler method is the oldest and simplest of the extrapolation procedures, and the least accurate for a given independent-variable step size. Its usefulness as a simulator method arises from its simplicity. The Runge–Kutta methods range in accuracy from slightly better than Euler to the limits...
The relaxation matrix, G, is an m×m square matrix that has m linearly independent eigenvectors vk. Therefore, we can express the starting error of the iteration process by linear combination of these eigenvectors, i.e. (5.41)ε(0)=∑k=1mckvk where ck are constant coefficients that are ...